What is the relationship between climate, vegetation and pollen in the northeastern United States? How does our definition of 'baseline' vegetation affect our efforts to model vegetation response to changing climate? Can statistical models relating pollen, vegetation and paleoclimatic change improve our ability to interpret vegetation responses to climate?

Goal: Develop accurate estimates of baseline, or potential, vegetation that account for the interaction between climate and vegetation over the last 2000 years.

Develop estimates of pre-settlement vegetation for the northwestern United States including forest composition, basal area and stem density based on established datasets.

Improve age-depth models for sedimentary pollen records in the region within a Bayesian framework to improve estimates of the pollen-vegetation relationship.

Estimate spatial processes that structure vegetation communities across the upper Midwestern United States using a Bayesian multinomial framework.

Figure 1: The PalEON domain and pollen sites used for analysis. Pollen sites are divided by substrate type.

Figure 2: Forest openness in Wisconsin and Minnesota estimated using historical survey data from the Public Lands Survey.From PalEON, unpublished.

Terrestrial ecosystem models are fundamental to forecasting ecological responses to 21st-century global change drivers, yet their predictions are alarmingly divergent and their ability to simulate decadal- to centennial-scale processes remains largely untested. Moreover, many future ecological simulations begin with an assumed pre-settlement baseline of "potential vegetation" that is stable and in equilibrium with climate -- an assumption that is likely invalid. Paleoecological and paleoclimatic records over the last 2000 years suggest that forests from New England to the Upper Midwest were characterized by broad scale compositional shifts, including regional responses to abrupt large-scale droughts and the immigration of new species. Because of the slow biotic and abiotic responses in forests and internal feedbacks, such processes can affect ecosystem dynamics for centuries. The rich paleo-datasets documenting these past dynamics offer an unexploited opportunity to better test and parameterize ecosystem models.

PalEON (the PaleoEcological Observatory Network) will establish an interdisciplinary team of paleoecologists, ecological statisticians, and ecosystem modelers with the goal of reconstructing forest composition, fire regime, and climate in forests across the northeastern US over the past 2000 years and applying them to drive and validate terrestrial ecosystem models. We will develop a coherent spatiotemporal inference framework to quantify trends and extreme events in paleoecological and paleoclimatic time series. Variables such as forest composition, fire regime, and moisture balance will be inferred from corresponding paleoecological proxies, with rigorous estimates of uncertainty. These datasets will be applied to improve terrestrial ecosystem models in two contexts. First, we will develop specific data products, such as high- resolution settlement-era forest composition maps from witness tree and GLO data, that can be used to drive ecosystem models. PalEON will develop formal data assimilation tools that will allow the models we use to forecast on centennial scales to be informed by decadal- to centennial-scale data. Second, we will develop data products for the purpose of model validation (e.g. fire-frequency reconstructions from sedimentary charcoal data). These long-term validation datasets will help us assess the ability of these models to capture past dynamics correctly, and will help us understand why their future projections are so divergent.

At Wisconsin, we are co-leading the effort to assemble the paleovegetational and paleoclimatic datasets and are working closely with the statisticians and modelers to generate spatial maps of forest composition before and at the moment of European settlement.&nbsp; We are also conducting a review of lake sedimentation rates in eastern North America in order to establish prior estimates for Bayesian age models.